Adjuvants have long been critical components of vaccines, but the exact mechanisms of their action and precisely how they alter or enhance vaccine-induced immune responses are often unclear. In this study, we used broad immunoprofiling of antibody, cellular, and cytokine responses, combined with data integration and machine learning to gain insight into the impact of different adjuvant formulations on vaccine-induced immune responses. A Self-Assembling Protein Nanoparticles (SAPN) presenting the malarial circumsporozoite protein (CSP) was used as a model vaccine, adjuvanted with three different liposomal formulations: liposome plus Alum (ALFA), liposome plus QS21 (ALFQ), and both (ALFQA). Using a computational approach to integrate the immunoprofiling data, we identified distinct vaccine-induced immune responses and developed a multivariate model that could predict the adjuvant condition from immune response data alone with 92% accuracy (p = 0.003). The data integration also revealed that commonly used readouts (i.e. serology, frequency of T cells producing IFN-γ, IL2, TNFα) missed important differences between adjuvants. In summary, broad immune-profiling in combination with machine learning methods enabled the reliable and clear definition of immune signatures for different adjuvant formulations, providing a means for quantitatively characterizing the complex roles that adjuvants can play in vaccine-induced immunity. The approach described here provides a powerful tool for identifying potential immune correlates of protection, a prerequisite for the rational pairing of vaccines candidates and adjuvants.

The somatic diversity of antigen-recognizing B-cell receptors (BCRs) arises from VDJ recombination and somatic hypermutation (SHM) during B cell development and affinity maturation. The VDJ junction of the BCR heavy chain forms the highly variable CDR3 region, which plays a critical role in antigen-specificity and binding affinity. Tracking the selection and mutation of the CDR3 region can be useful in characterizing humoral responses to infection and vaccination. Although tens to hundreds of thousands of unique BCR genes within an expressed B-cell repertoire can now be resolved with high-throughput sequencing, tracking SHMs is still challenging because existing annotation methods are often limited by poor annotation coverage, inconsistent SHM identification across the VDJ junction, or lack of B-cell lineage data. Here, we present BRILIA (B-cell repertoire inductive lineage and immunosequence annotator), an algorithm that leverages repertoire-wide sequencing data to globally improve the VDJ annotation coverage, lineage tree assembly, and SHM identification. On benchmark tests against simulated human and mouse BCR repertoires, BRILIA correctly annotated germline and clonally expanded sequences with 94% and 70% accuracy, respectively, and it has a 90% SHM positive prediction rate in the CDR3 region of heavily mutated sequences; these are substantial improvements over existing methods. We used BRILIA to process BCR sequences obtained from splenic germinal center B cells extracted from C57BL/6 mice. BRILIA returned robust B-cell lineage trees and yielded SHM patterns that are consistent across the VDJ junction and agree with known biological mechanisms of SHM. By contrast, existing BCR annotation tools, which do not account for repertoire-wide clonal relationships, systematically underestimated both the size of clonally related B cell clusters and yielded inconsistent SHM frequencies. We demonstrate BRILIA’s utility in B cell repertoire studies related to VDJ gene usage, mechanisms for adenosine mutations, and SHM hot spot motifs. Furthermore, we show that the complete gene usage annotation and SHM identification across the entire CDR3 are essential for studying the B cell affinity maturation process through immunosequencing methods.

High-throughput B-cell sequencing has opened up new avenues for investigating complex mechanisms underlying our adaptive immune response. These technological advances drive data generation and the need to mine and analyze the information contained in these large datasets, in particular the identification of therapeutic antibodies (Abs) or those associated with disease exposure and protection. Here, we describe our efforts to use artificial intelligence (AI)-based image-analyses for prospective classification of Abs based solely on sequence information. We hypothesized that Abs recognizing the same part of an antigen share a limited set of features at the binding interface, and that the binding site regions of these Abs share share common structure and physicochemical property patterns that can serve as a “fingerprint” to recognize uncharacterized Abs. We combined large-scale sequence-based protein-structure predictions to generate ensembles of 3-D Ab models, reduced the Ab binding interface to a 2-D image (fingerprint), used pre-trained convolutional neural networks to extract features, and trained deep neural networks (DNNs) to classify Abs. We evaluated this approach using Ab sequences derived from human HIV and Ebola viral infections to differentiate between two Abs, Abs belonging to specific B-cell family lineages, and Abs with different epitope preferences. In addition, we explored a different type of DNN method to detect one class of Abs from a larger pool of Abs. Testing on Ab sets that had been kept aside during model training, we achieved average prediction accuracies ranging from 71–96% depending on the complexity of the classification task. The high level of accuracies reached during these classification tests suggests that the DNN models were able to learn a series of structural patterns shared by Abs belonging to the same class. The developed methodology provides a means to apply AI-based image recognition techniques to analyze high-throughput B-cell sequencing datasets (repertoires) for Ab classification.

Adjuvants produce complex, but often subtle, effects on vaccine-induced immune responses that, nonetheless, play a critical role in vaccine efficacy. In-depth profiling of vaccine-induced cytokine, cellular, and antibody responses (“immunoprofiling”) combined with machine-learning holds the promise of identifying adjuvant-specific immune response characteristics that can guide rational adjuvant selection. Here, we profiled human immune responses induced by vaccines adjuvanted with two similar, clinically relevant adjuvants, AS01B and AS02A, and identified key distinguishing characteristics, or immune signatures, they imprint on vaccine-induced immunity. Samples for this side-by-side comparison were from malaria-naïve individuals who had received a recombinant malaria subunit vaccine (AMA-1) that targets the pre-erythrocytic stage of the parasite. Both adjuvant formulations contain the same immunostimulatory components, QS21 and MPL, thus this study reveals the subtle impact that adjuvant formulation has on immunogenicity. Adjuvant-mediated immune signatures were established through a two-step approach: First, we generated a broad immunoprofile (serological, functional and cellular characterization of vaccine-induced responses). Second, we integrated the immunoprofiling data and identify what combination of immune features was most clearly able to distinguish vaccine-induced responses by adjuvant using machine learning. The computational analysis revealed statistically significant differences in cellular and antibody responses between cohorts and identified a combination of immune features that was able to distinguish subjects by adjuvant with 71% accuracy. Moreover, the in-depth characterization demonstrated an unexpected induction of CD8⁺ T cells by the recombinant subunit vaccine, which is rare and highly relevant for future vaccine design.

A majority infections caused by dengue virus (DENV) are asymptomatic, but a higher incidence of severe illness, such as dengue hemorrhagic fever, is associated with secondary infections, suggesting that pre-existing immunity plays a central role in dengue pathogenesis. Primary infections are typically associated with a largely serotype-specific antibody response, while secondary infections show a shift to a broadly cross-reactive antibody response. We hypothesized that the basis for the shift in serotype-specificity between primary and secondary infections can be found in a change in the antibody fine-specificity. To investigate the link between epitope- and serotype-specificity, we assembled the Dengue Virus Antibody Database, an online repository containing over 400 DENV-specific mAbs, each annotated with information on 1) its origin, including the immunogen, host immune history, and selection methods, 2) binding/neutralization data against all four DENV serotypes, and 3) epitope mapping at the domain or residue level to the DENV E protein. We combined epitope mapping and activity information to determine a residue-level index of epitope propensity and cross-reactivity and generated detailed composite epitope maps of primary and secondary antibody responses. We found differing patterns of epitope-specificity between primary and secondary infections, where secondary responses target a distinct subset of epitopes found in the primary response. We found that secondary infections were marked with an enhanced response to cross-reactive epitopes, such as the fusion-loop and E-dimer region, as well as increased cross-reactivity in what are typically more serotype-specific epitope regions, such as the domain I-II interface and domain III. Our results support the theory that pre-existing cross-reactive memory B cells form the basis for the secondary antibody response, resulting in a broadening of the response in terms of cross-reactivity, and a focusing of the response to a subset of epitopes, including some, such as the fusion-loop region, that are implicated in poor neutralization and antibody-dependent enhancement of infection. Dengue virus (DENV) infections are typically asymptomatic, but severe and potentially lethal disease symptoms, such as dengue hemorrhagic fever, are associated with secondary infections. This suggests that pre-existing immunity from primary infection plays a central role in DENV pathogenesis. In order to characterize the antibody response in primary and secondary infections, we assembled the Dengue Virus Antibody Database, a freely accessible online repository (http://denvabdb.bhsai.org) storing over 400 unique monoclonal dengue-specific antibodies annotated by their 1) origin and host immune history, 2) activity information against all four dengue serotypes, and 3) epitope mapping information. Here we demonstrate the utility of the database by carrying out a large-scale analysis to characterize shifts in epitope fine-specificity and serotype cross-reactivity in primary and secondary infections. In particular, we show how the antibody response in secondary infections displays a systematic shift towards increased serotype cross-reactivity by focusing on a subset of cross-reactive epitopes on the dengue E protein. Our findings suggest a mechanistic basis for this shift in epitope and serotype specificity and demonstrate how a detailed understanding of the antibody response can provide insight into the mechanisms of dengue pathogenesis.

Glycans attached to the cell surface via proteins or lipids or exposed in the extracellular matrix affect many cellular processes, including neuritogenesis, cell survival and migration, as well as synaptic activity and plasticity. These functions make glycans attractive molecules for stimulating repair of the injured nervous system. Yet, glycans are often difficult to synthesize or isolate and have the disadvantage to be unstable in a complex tissue environment. To circumvent these issues, we have screened a library of small organic compounds to search for structural and functional mimetics of the neurostimulatory glycan polysialic acid (PSA) and identified the 5-HT₄ receptor agonist tegaserod as a PSA mimetic. The PSA mimicking activity of tegaserod was shown in cultures of central and peripheral nervous system cells of the mouse and found to be independent of its described function as a serotonin (5-HT₄) receptor agonist. In an in vivo model for peripheral nerve regeneration, mice receiving tegaserod at the site of injury showed enhanced recovery compared to control mice receiving vehicle control as evidenced by functional measurements and histology. These data indicate that tegaserod could be repurposed for treatment of nervous system injuries and underscores the potential of using small molecules as mimetics of neurostimulatory glycans.